Technology leaders are under growing pressure to justify cloud and AI spending in business terms, not just track costs. As organisations scale hybrid and multi-cloud environments alongside AI initiatives, many still rely on outdated planning processes and fragmented tools that leave significant blind spots, according to IBM Apptio's latest research.
Pete Wilson, Vice President and General Manager of IBM Apptio Business APAC, told iTNews Asia that the primary mandate for tech leaders has shifted from cost control to proving value. "Technology leaders today are under growing pressure to explain not just how much they are spending, but why that spending makes sense in business terms," Wilson said.
The forecasting confidence gap
IBM Apptio's 2026 global Technology Investment Management report found that about half of respondents report low confidence in cloud spend forecasting. Only 13 percent of FinOps professionals have fully allocated AI and machine-learning cloud costs back to the business with optimisation insights visible. The rest are still exploring AI cost management or allocating costs without optimisation visibility.
This uncertainty has real consequences. Over the past 12 months, organisations have seen significant cost increases and overruns driven by AI investments. When forecasting accuracy is limited, leaders spend more time reconciling variance than directing investment toward growth initiatives.
Wilson recommends a practical starting point: track public cloud spend weekly or even daily. "Weekly, if not, daily tracking of your public cloud spend will show early signs of cost growth driven by the growth of AI usage in your organisation," he said.
Why outdated planning persists
The root cause runs deeper than resistance to change. Almost half of organisations primarily use ERP systems to manage IT spend and budgets, while only 7 percent rely on spreadsheets. About a third use purpose-built IT financial management platforms. ERP systems run traditional CAPEX planning cycles that were designed for stable, asset-based environments - not the consumption-driven, variable-cost reality of cloud and AI.
"The forcing function for change is the increasing pressure on IT and Finance to do more with the same investment - drive down 'Run' costs to enable funding of 'Growth' initiatives," Wilson said.
This mismatch between planning cycles and actual spending behaviour creates friction. Two out of three organisations polled are funding initiatives primarily from existing budgets rather than new investment, increasing pressure to reprioritise continuously rather than plan once a year.
Governance and allocation gaps
While nine out of 10 leaders rank AI as a priority investment area, the challenge emerges when technologies move beyond experimentation to enterprise-wide deployment. Most organisations rate their FinOps maturity as established or higher, yet only about one in 10 have achieved full chargeback for cloud costs.
Wilson pointed to chargeback frequency as a persistent issue. "To drive true accountability of costs - Cloud or AI or both - you must charge on a monthly basis with a completely digital based bill," he said. This means business consumers can self-serve full details of charges, including units consumed and price per unit, creating 100 percent transparency into IT consumption and costs.
Without clear governance and allocation, optimisation becomes reactive. Leaders can see costs rising but struggle to explain which workloads, applications, or decisions are driving those increases, or how they relate to business outcomes.
Restoring decision confidence
Wilson identified a persistent mismatch between perceived and actual IT financial management capability. Nearly three in five IT financial management professionals believe their forecasts are highly accurate, yet only about a third use purpose-built tools to manage and validate technology spend.
Closing this gap requires integrated, accurate data normalised across finance, IT, and the business. "The hardest struggle today is clearly when CIO and CFO do not have this level of transparency leading to 'best guesses' of what and where to invest," Wilson said.
Organisations making progress recognise a dual mandate: continue investing in priority areas such as AI and cybersecurity while exercising stronger financial discipline. For professionals working in AI for Management roles, this means developing the capability to tie investment spend directly to business value - understanding the total cost of an investment and measuring that against outcomes.
Wilson's recommendation on a single unifying metric cuts through the complexity. "If I had to settle on one single metric across IT, Finance and Business for Cloud and AI investment, I would choose the ongoing operating cost of the cloud/AI solution in question."
Why this matters for management
The data points to a clear risk: overall technology spend may continue to rise while its effectiveness declines. Decision-making slows as leaders lose confidence that the numbers reflect current conditions. Approvals take longer, governance layers increase, and innovation becomes harder to sustain. For managers overseeing cloud and AI initiatives, the priority is building foundational IT financial management capabilities - unified cost, usage, and delivery insights - before scaling further. Without consistent cost transparency and forecast accuracy, the ability to make informed trade-offs across complex portfolios erodes quickly. For those managing budgets where cloud and AI costs intersect with traditional finance systems, AI for Finance skills are becoming essential to closing the gap between spend visibility and business value.
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